Discrimination of moldy peanuts with reference to aflatoxin using FTIR-ATR system

Hande Kaya-Celiker, P. Kumar Mallikarjunan, David Schmale, Maria Elisa Christie

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


This study demonstrated the potential use of Fourier transform infrared spectroscopy, coupled with attenuated total reflectance unit (FTIR-ATR) for determination of aflatoxigenic and non-aflatoxigenic strains of Aspergillus flavus and Aspergillus parasiticus invasion in peanuts. Threshold mold density on peanut paste samples was 2.7LogCFU/g peanut corresponding to legislative limiting aflatoxin (AF) level of 20ppb. Classification was performed to separate the "Acceptable" stream (AF≤20ppb) from so called "Moldy" (20<AF<1200ppb) and "Highly Moldy" (>1200ppb). All of the samples (n=164) were classified correctly when discriminant analysis technique was employed. Second threshold value was set at 300ppb aflatoxin to further sort out the samples in the "Moldy" class into mildly (which can be used for feed) or highly toxic (which has to be discarded). Correct separation was observed at 98.5% with only one misclassified sample. Growth profiles of both strains of A.flavus and A.parasiticus were interpreted with respect to spectral changes. Even when spectral alterations for aflatoxin presence were not clearly identifiable, similar secondary metabolites of both aflatoxigenic species led to clusters in distance plots and showed the potential usage of the developed method to separate safer peanuts (e.g. AF ≤ 20ppb) in a lot when implemented. Partial least squares (PLS) regression models were developed to predict AF level with maximum correlation coefficient of determination (RC2=99.98% for both AF-producing Aspergillus spp.). The fingerprint region (1800-800cm-1) was used for regression analysis and corresponding bands were interpreted.

Original languageEnglish (US)
Pages (from-to)64-71
Number of pages8
JournalFood Control
StatePublished - Oct 2014

Bibliographical note

Funding Information:
Authors wish to express their gratitude to Project VT-134 , Peanut Collaborative Research Support Program funded by USAID for providing the financial support to the project. Authors also would like to extend their gratitude to Drs. Justin Barone, Archileo Kaaya and Charity Mutegi for their participation in the project.

Copyright 2014 Elsevier B.V., All rights reserved.


  • A spergillus flavus
  • A spergillus parasiticus
  • Discriminant analysis
  • PLS regression
  • Peanut

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